Evidence map›Paper›PMID 36593882›Full record

ArticleJournal of computational social science2023

An AI-based framework for studying visual diversity of urban neighborhoods and its relationship with socio-demographic variables.

Md Amiruzzaman, Ye Zhao, Stefanie Amiruzzaman, Aryn C Karpinski, Tsung Heng Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of computational social science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Md AmiruzzamanDepartment of Computer Science, West Chester University, West Chester, PA USA.ORCID 0000-0002-2292-5798
Ye ZhaoDepartment of Computer Science, Kent State University, Kent, OH USA.
Stefanie AmiruzzamanDepartment of Languages and Cultures, West Chester University, West Chester, PA USA.
Aryn C KarpinskiResearch, Measurement & Statistics, Kent State University, Kent, OH USA.
Tsung Heng WuDepartment of Computer Science, Kent State University, Kent, OH USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents a framework to study quantitatively geographical visual diversities of urban neighborhood from a large collection of street-view images using an Artificial Intelligence (AI)-based image segmentation technique. A variety of diversity indices are computed from the extracted visual semantics. They are utilized to discover the relationships between urban visual appearance and socio-demographic variables. This study also validates the reliability of the method with human evaluators. The methodology and results obtained from this study can potentially be used to study urban features, locate houses, establish services, and better operate municipalities.

Indexed as

Google street-viewInterrater reliabilitySemantic segmentationSocial phenomenaUrban neighborhoodVisual diversity

Identifiers

PMID36593882
PMCPMC9795947

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.